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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.19825 |
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| _version_ | 1866929402737065984 |
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| author | Cauz, Marine Bolland, Adrien Wyrsch, Nicolas Ballif, Christophe |
| author_facet | Cauz, Marine Bolland, Adrien Wyrsch, Nicolas Ballif, Christophe |
| contents | The ongoing energy transition drives the development of decentralised renewable energy sources, which are heterogeneous and weather-dependent, complicating their integration into energy systems. This study tackles this issue by introducing a novel reinforcement learning (RL) framework tailored for the co-optimisation of design and control in energy systems. Traditionally, the integration of renewable sources in the energy sector has relied on complex mathematical modelling and sequential processes. By leveraging RL's model-free capabilities, the framework eliminates the need for explicit system modelling. By optimising both control and design policies jointly, the framework enhances the integration of renewable sources and improves system efficiency. This contribution paves the way for advanced RL applications in energy management, leading to more efficient and effective use of renewable energy sources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_19825 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems Cauz, Marine Bolland, Adrien Wyrsch, Nicolas Ballif, Christophe Machine Learning The ongoing energy transition drives the development of decentralised renewable energy sources, which are heterogeneous and weather-dependent, complicating their integration into energy systems. This study tackles this issue by introducing a novel reinforcement learning (RL) framework tailored for the co-optimisation of design and control in energy systems. Traditionally, the integration of renewable sources in the energy sector has relied on complex mathematical modelling and sequential processes. By leveraging RL's model-free capabilities, the framework eliminates the need for explicit system modelling. By optimising both control and design policies jointly, the framework enhances the integration of renewable sources and improves system efficiency. This contribution paves the way for advanced RL applications in energy management, leading to more efficient and effective use of renewable energy sources. |
| title | Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.19825 |